Philippine vehicle plate localization using image thresholding and genetic algorithm

This paper proposes a vehicle plate localization method using genetic algorithm integrated with image thresholding. Image thresholding outputs a value which varies on the time the image is captured. Genetic algorithm on the other hand, executed the license plate region detection of the digital image...

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Main Authors: Bedruz, Rhen Anjerome, Sybingco, Edwin, Bandala, Argel A., Quiros, Ana Riza, Uy, Aaron Christian P., Dadios, Elmer P.
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Published: Animo Repository 2017
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Online Access:https://animorepository.dlsu.edu.ph/faculty_research/3356
https://animorepository.dlsu.edu.ph/context/faculty_research/article/4358/type/native/viewcontent/TENCON.2016.7848557
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Institution: De La Salle University
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spelling oai:animorepository.dlsu.edu.ph:faculty_research-43582023-01-09T09:26:31Z Philippine vehicle plate localization using image thresholding and genetic algorithm Bedruz, Rhen Anjerome Sybingco, Edwin Bandala, Argel A. Quiros, Ana Riza Uy, Aaron Christian P. Dadios, Elmer P. This paper proposes a vehicle plate localization method using genetic algorithm integrated with image thresholding. Image thresholding outputs a value which varies on the time the image is captured. Genetic algorithm on the other hand, executed the license plate region detection of the digital image which depends on the set-level of the image threshold values obtained. Using the proposed algorithm, it was shown how the algorithm was effective on finding the plate location in a given image. Results show that the different parameters tested were successful and converges to a point where the plate locations can be located. The algorithms were tested on an image of a vehicle equipped with a license plate on its frontal view tested on a large number of trials. The genetic algorithm initialized 2000 chromosomes as its initial population and a fixed generation's count of 100. It was observed that the time it took for the program to locate the plate is about 3 seconds. Another finding observed is that by varying the initial chromosome count and generation count will lead to longer computation time with increased accuracy. On the contrary, if the initial values were lessened, computation time will be less but the accuracy lessen. Results show that this plate localization technique successfully locates the plate and may be calibrated depending on the time of analysis. © 2016 IEEE. 2017-02-08T08:00:00Z text text/html https://animorepository.dlsu.edu.ph/faculty_research/3356 info:doi/10.1109/TENCON.2016.7848557 https://animorepository.dlsu.edu.ph/context/faculty_research/article/4358/type/native/viewcontent/TENCON.2016.7848557 Faculty Research Work Animo Repository Vehicle detectors Genetic algorithms Image processing Manufacturing
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
topic Vehicle detectors
Genetic algorithms
Image processing
Manufacturing
spellingShingle Vehicle detectors
Genetic algorithms
Image processing
Manufacturing
Bedruz, Rhen Anjerome
Sybingco, Edwin
Bandala, Argel A.
Quiros, Ana Riza
Uy, Aaron Christian P.
Dadios, Elmer P.
Philippine vehicle plate localization using image thresholding and genetic algorithm
description This paper proposes a vehicle plate localization method using genetic algorithm integrated with image thresholding. Image thresholding outputs a value which varies on the time the image is captured. Genetic algorithm on the other hand, executed the license plate region detection of the digital image which depends on the set-level of the image threshold values obtained. Using the proposed algorithm, it was shown how the algorithm was effective on finding the plate location in a given image. Results show that the different parameters tested were successful and converges to a point where the plate locations can be located. The algorithms were tested on an image of a vehicle equipped with a license plate on its frontal view tested on a large number of trials. The genetic algorithm initialized 2000 chromosomes as its initial population and a fixed generation's count of 100. It was observed that the time it took for the program to locate the plate is about 3 seconds. Another finding observed is that by varying the initial chromosome count and generation count will lead to longer computation time with increased accuracy. On the contrary, if the initial values were lessened, computation time will be less but the accuracy lessen. Results show that this plate localization technique successfully locates the plate and may be calibrated depending on the time of analysis. © 2016 IEEE.
format text
author Bedruz, Rhen Anjerome
Sybingco, Edwin
Bandala, Argel A.
Quiros, Ana Riza
Uy, Aaron Christian P.
Dadios, Elmer P.
author_facet Bedruz, Rhen Anjerome
Sybingco, Edwin
Bandala, Argel A.
Quiros, Ana Riza
Uy, Aaron Christian P.
Dadios, Elmer P.
author_sort Bedruz, Rhen Anjerome
title Philippine vehicle plate localization using image thresholding and genetic algorithm
title_short Philippine vehicle plate localization using image thresholding and genetic algorithm
title_full Philippine vehicle plate localization using image thresholding and genetic algorithm
title_fullStr Philippine vehicle plate localization using image thresholding and genetic algorithm
title_full_unstemmed Philippine vehicle plate localization using image thresholding and genetic algorithm
title_sort philippine vehicle plate localization using image thresholding and genetic algorithm
publisher Animo Repository
publishDate 2017
url https://animorepository.dlsu.edu.ph/faculty_research/3356
https://animorepository.dlsu.edu.ph/context/faculty_research/article/4358/type/native/viewcontent/TENCON.2016.7848557
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